Method and apparatus for processing block to be processed of urine sediment image

ABSTRACT

Provided in the present invention are a method and apparatus for processing a block to be processed of a urine sediment image. The method comprises: calculating a variable number of local feature vectors of a block to be processed, wherein the local feature vector is a vector representing a feature of a local location of the block to be processed; classifying the variable number of local feature vectors into a fixed number of clusters so as to obtain a statistical histogram vector of the fixed number of clusters of the block to be processed, wherein the statistical histogram vector reflects the statistical distribution of the variable number of local feature vectors in the fixed number of clusters; and taking the statistical histogram vector as a feature in a feature set of block processing and processing the block to be processed.

The subject application claims benefit under 35 USC §119(e) of ChinesePatent Application No. 201410183665.0, filed Apr. 30, 2014. The entirecontents of the above-referenced patent application are hereby expresslyincorporated herein by reference.

TECHNICAL FIELD

The present invention relates to biological detection, and in particularto a method and apparatus for processing a block to be processed of aurine sediment image.

BACKGROUND ART

In common urine sediment analysis, first, a urine sample image is shotusing a microscope imaging system. Then, the candidate blocks in theurine sample image are segmented using, for example, an edge detectiontechnology. By removing obvious background blocks from these candidateblocks, blocks to be processed are detected. Next, the blocks to beprocessed are processed.

Currently, there are mainly two directions of processing the blocks tobe processed. The first direction is classification, i.e. directlyclassifying these blocks to be processed into various visible element(such as a cast, an epithelium and an erythrocyte) blocks and backgroundblocks that are easy to be confused with visible elements. The otherdirection is block retrieval, which does not directly classify theblocks to be processed but retrieves blocks similar to the previouslystored blocks to be processed in a database. The unique difference withregard to the result of classification lies in that block retrieval mayretrieve a plurality of similar blocks to be provided to a user, andthus can provide more information for the user. The user may perform afurther selection or judgment in the plurality of similar blocks.

The classification and block retrieval achieved by a machineautomatically generally use an approach of machine learning. Severalfeatures for classification or block retrieval are specified andconstitute a feature set. A large number of training sample blocks arefirstly used to constitute a training sample set for training aprocessing model (a classification model or a block retrieval model).With regard to each training sample block in the training sample set,the features in the feature set are calculated for the processing modelto learn. In this way, when the trained processing model receives a newblock to be processed, the features in the feature set are calculatedfor the new block to be processed, and according to the calculatedfeatures in the feature set and with reference to the previous learningresult, classification can be performed thereon or the previously storedsimilar images are retrieved therefor.

CONTENTS OF THE INVENTION

One embodiment of the present invention aims to improve the precision ofprocessing a block to be processed of a urine sediment image.

According to one embodiment of the present invention, a method forprocessing a block to be processed of a urine sediment image isprovided, comprising: calculating a variable number of local featurevectors of a block to be processed, wherein the local feature vector isa vector representing a feature of a local location of the block to beprocessed; classifying the variable number of local feature vectors intoa fixed number of clusters so as to obtain a statistical histogramvector of the fixed number of clusters of the block to be processed,wherein the statistical histogram vector reflects the statisticaldistribution of the variable number of local feature vectors in thefixed number of clusters; and taking the statistical histogram vector asa feature in a feature set of block processing and processing the blockto be processed.

In a particular implementation, the variable number of local featurestatistical vectors comprise m scale invariant feature transform (SIFT)feature vectors and n local binary pattern (LBP) feature vectors, wherem and n are variable positive integers. The step of classifying thevariable number of local feature vectors into a fixed number of clusterscomprises: classifying the m SIFT feature vectors into k1 clusters andclassifying the n LBP feature vectors into k2 clusters so as to obtain astatistical histogram vector of the k1 clusters of the SIFT featurevectors and a statistical histogram vector of the k2 clusters of the LBPfeature vectors, where k1 and k2 are fixed positive integers.

In a particular implementation, the step of taking the statisticalhistogram vector as a feature in a feature set of block processing andprocessing the block to be processed comprises: combining thestatistical histogram vector of the k1 clusters of the SIFT featurevectors and the statistical histogram vector of the k2 clusters of theLBP feature vectors; and taking the combined statistical histogramvector as a feature in a feature set of block processing and processingthe block to be processed.

In a particular implementation, the step of calculating a variablenumber of local feature vectors of a block to be processed comprises:converting the block to be processed into a grayscale block to beprocessed, and calculating a variable number of local featurestatistical vectors of the grayscale block to be processed.

In a particular implementation, the block to be processed comprises acast, an epithelium and a block of a background that is easy to beconfused with a visible element.

In a particular implementation, in the step of calculating a variablenumber of local feature vectors of a block to be processed, the m SIFTfeature vectors of the block to be processed are calculated as follows:applying a successive Gaussian filter to the block to be processed aplurality of times and/or a scaled block to be processed so as to obtaina multi-layer block; by calculating a difference of various pixel valuesof an adjacent-layer block in the multi-layer block, obtaining aGaussian difference block between adjacent-layer blocks so as to form amulti-layer Gaussian difference block; with regard to a specific pixelin a specific-layer Gaussian difference block in the multi-layerGaussian difference block, judging whether the value of the specificpixel is a maximum pixel value or a minimum pixel value in a3-pixel×3-pixel×3-pixel cube with the specific pixel as the center ofthe specific-layer Gaussian difference block, an upper-layer Gaussiandifference block and a lower-layer Gaussian difference block, and ifyes, marking the location of the specific pixel as a key point, whereinwith regard to the multi-layer Gaussian difference block, m key pointsare marked together; calculating a gradient value and a gradientdirection of a pixel in a specific adjacent area in a plurality ofadjacent areas of the key point, wherein the gradient direction isapproximated to one of a pre-specified plurality of standard directions;and calculating a total gradient value of a pixel in the specificadjacent area of the key point in a specific standard direction to betaken as a component of the SIFT feature vector of the key point in thespecific adjacent area in the specific standard direction.

In a particular implementation, in the step of calculating a variablenumber of local feature vectors of a block to be processed, the n LBPfeature vectors of the block to be processed are calculated as follows:with regard to a pixel in the block to be processed, by comparing anintensity value of the pixel with the intensity values of the otherpixels in a 3-pixel×3-pixel square with the pixel as the center, formingan 8-bit LBP code composed of 0 or 1, wherein a value range of the LBPcode is an integer from 0 to 255; segmenting the block to be processedinto windows with a fixed size, wherein the block is segmented into nwindows with a fixed size; and calculating an LBP feature vector of eachwindow, wherein the LBP feature vector is a distribution histogram from0 to 255 of an LBP code value of a pixel in the window.

According to one embodiment of the present invention, an apparatus forprocessing a block to be processed of a urine sediment image is furtherprovided, comprising: a calculation unit configured to calculate avariable number of local feature vectors of a block to be processed,wherein the local feature vector is a vector representing a feature of alocal location of the block to be processed; a classification unitconfigured to classify the variable number of local feature statisticalvectors into a fixed number of clusters so as to obtain a statisticalhistogram vector of the fixed number of clusters of the block to beprocessed, wherein the statistical histogram vector reflects thestatistical distribution of the variable number of local featurestatistical vectors in the fixed number of clusters; and a processingunit configured to take the statistical histogram vector as a feature ina feature set of block processing and process the block to be processed.

In a particular implementation, the variable number of local featurevectors comprise m SIFT feature vectors and n LBP feature vectors, wherem and n are variable positive integers. The classification unit isfurther configured to: classify the m SIFT feature vectors into k1clusters and classify the n LBP feature vectors into k2 clusters so asto obtain a statistical histogram vector of the k1 clusters of the SIFTfeature vectors and a statistical histogram vector of the k2 clusters ofthe LBP feature vectors, where k1 and k2 are fixed positive integers.

In a particular implementation, the processing unit is furtherconfigured to: combine the statistical histogram vector of the k1clusters of the SIFT feature vectors and the statistical histogramvector of the k2 clusters of the LBP feature vectors; and take thecombined statistical histogram vector as a feature in a feature set ofblock processing and process the block to be processed.

In a particular implementation, the calculation unit is furtherconfigured to: convert the block to be processed into a grayscale blockto be processed, and calculate a variable number of local featurevectors of the grayscale block to be processed.

In a particular implementation, the block to be processed comprises acast, an epithelium and a block of a background that is easy to beconfused with a visible element.

In a particular implementation, the calculation unit is furtherconfigured to calculate the m SIFT feature vectors of the block to beprocessed as follows: applying a successive Gaussian filter to the blockto be processed a plurality of times and/or a scaled block to beprocessed so as to obtain a multi-layer block; by calculating adifference of various pixel values of an adjacent-layer block in themulti-layer block, obtaining a Gaussian difference block betweenadjacent-layer blocks so as to form a multi-layer Gaussian differenceblock; with regard to a specific pixel in a specific-layer Gaussiandifference block in the multi-layer Gaussian difference block, judgingwhether the value of the specific pixel is a maximum pixel value or aminimum pixel value in a 3-pixel×3-pixel×3-pixel cube with the specificpixel as the center of the specific-layer Gaussian difference block, anupper-layer Gaussian difference block and a lower-layer Gaussiandifference block, and if yes, marking the location of the specific pixelas a key point, wherein with regard to the multi-layer Gaussiandifference block, m key points are marked together; calculating agradient value and a gradient direction of a pixel in a specificadjacent area in a plurality of adjacent areas of the key point, whereinthe gradient direction is approximated to one of pre-specified aplurality of standard directions; and calculating a total gradient valueof a pixel in the specific adjacent area of the key point in a specificstandard direction to be taken as a component of the SIFT feature vectorof the key point in the specific adjacent area in the specific standarddirection, with the SIFT feature vector being a statistical histogram ofthe total gradient value of the pixel in each standard direction in eachadjacent area of the key point.

In a particular implementation, the calculation unit is furtherconfigured to calculate the n LBP feature vectors of the block to beprocessed as follows: with regard to a pixel in the block to beprocessed, by comparing an intensity value of the pixel with theintensity values of the other pixels in a 3-pixel×3-pixel square withthe pixel as the center, forming an 8-bit LBP code composed of 0 or 1,wherein a value range of the LBP code is an integer from 0 to 255;segmenting the block to be processed into windows with a fixed size,wherein the block is segmented into n windows with a fixed size; andcalculating an LBP feature vector of each window, wherein the LBPfeature vector is a distribution histogram from 0 to 255 of an LBP codevalue of a pixel in the window.

Most of the features in a feature set in the prior art focus on togeneral features of a block to be processed, such as general size, shapeand appearance, etc. of a cell, but for some visible elements (such as acast and an epithelium), their general features always have a largeintra-class difference and a high inter-class similarity, and thus it isnot easy to obtain satisfying processing precision. Since theembodiments of the present invention aim at local features and the localfeatures can reduce the intra-class difference and present aninter-class difference, the precision of processing a block to beprocessed is greatly improved. Since the embodiments of the presentinvention use a variable number of local feature vectors according todifferent information quantities contained in the blocks to beprocessed, more local feature vectors are used for a block to beprocessed containing a larger information quantity and less localfeature vectors are used for a block to be processed containing lessinformation quantity, such as a background. This approach improvesinformation utilization efficiency and improves the precision ofprocessing a block to be processed.

Since SIFT feature vectors and LBP feature vectors are used in aparticular implementation of the present invention, the SIFT featurevectors, when being used for processing a block to be processed, arevery robust for the size and direction, etc. of a visible element, andthe LBP feature vectors, when being used for processing a block to beprocessed, are very robust for illumination, etc., thereby greatlyimproving the precision of processing a block to be processed.

In addition, since in a particular implementation of the presentinvention, a block to be processed is firstly converted into a grayscaleblock to be processed, and then a local feature statistical vector ofthe grayscale block to be processed is calculated, which improves therobustness of the processing result for color, etc.

In addition, LBP feature vectors not only embody certain local featuresof a block but also embody certain general features of a block, and SIFTfeature vectors embody the local features of a block. Since in aparticular implementation of the present invention, the statisticalhistogram vector of k1 clusters of SIFT feature vectors and thestatistical histogram vector of k2 clusters of LBP feature vectors arecombined, by adjusting a ratio of k1 and k2, the contribution rate oflocal features and general features can be better adjusted forprocessing a block to be processed.

DESCRIPTION OF THE ACCOMPANYING DRAWINGS

These and other features and advantages of the present invention willbecome more apparent byway of the detailed description hereinbelow inconjunction with the accompanying drawings.

FIG. 1 shows a flowchart of a method for processing a block to beprocessed of a urine sediment image according to one embodiment of thepresent invention.

FIG. 2 shows a schematic diagram of a process of forming a multi-layerGaussian difference block according to one embodiment of the presentinvention.

FIG. 3 shows a schematic diagram of a process of forming a SIFT featurevector of a specific key point according to one embodiment of thepresent invention.

FIG. 4 shows a schematic diagram of all the SIFT feature vectors of ablock to be processed according to one embodiment of the presentinvention.

FIG. 5 shows an LBP code formed with regard to a specific pixel in ablock to be processed according to one embodiment of the presentinvention.

FIG. 6 shows a schematic diagram of an LBP feature vector of a specificwindow of a block to be processed according to one embodiment of thepresent invention.

FIG. 7 shows a schematic diagram of all the LBP feature vectors of ablock to be processed according to one embodiment of the presentinvention.

FIG. 8 shows a schematic diagram of a statistical histogram vector of ablock to be processed with regard to k1 clusters according to oneembodiment of the present invention.

FIG. 9 shows a block diagram of an apparatus for processing a block tobe processed of a urine sediment image according to one embodiment ofthe present invention.

FIG. 10 shows a block diagram of a device for processing a block to beprocessed of a urine sediment image according to one embodiment of thepresent invention.

DETAILED DESCRIPTION OF EMBODIMENTS

Various embodiments of the present invention will be described below indetail in combination with the accompanying drawings.

FIG. 1 shows a flowchart of a method 1 for processing a block to beprocessed of a urine sediment image according to one embodiment of thepresent invention.

In step S1, a variable number of local feature vectors of a block to beprocessed is calculated. General features are features that a block tobe processed has integrally, such as a general shape, etc. Localfeatures are features of a block to be processed at a local location,such as texture at the local location, etc. A local feature vector is avector representing the local features of a block to be processed, forexample, SIFT feature vectors and LBP feature vectors introduced below.

In step S2, the variable number of local feature vectors are classifiedinto a fixed number of clusters so as to obtain a statistical histogramvector of the fixed number of clusters of the block to be processed,i.e., a bag-of-words model (BOW). The statistical histogram vectorreflects the statistical distribution of the variable number of localfeature vectors in the fixed number of clusters. The reason forperforming step S2 is that the features in a feature set of blockprocessing must be universal for all the blocks to be processed. Only ifthe variable number of local feature vectors are classified into thefixed number of clusters so as to obtain the statistical histogramvector of the fixed number of clusters, can the statistical histogramvector have a comparability between various blocks to be processed.

In step S3, the statistical histogram vector is taken as a feature in afeature set of block processing, and the block to be processed isprocessed.

In one embodiment, the variable number of local feature vectors comprisem SIFT feature vectors and n LBP feature vectors, where m and n arevariable positive integers. The process of obtaining the statisticalhistogram vector after the block to be processed is clustered so as toprocess the block to be processed is described in detail below incombination with FIGS. 2-9.

Calculation of SIFT Feature Vectors:

Firstly, a successive Gaussian filter is applied to a block to beprocessed a plurality of times and/or a scaled block to be processed soas to obtain a multi-layer block.

FIG. 2 shows an example of multiple Gaussian filters. Gaussian filter isapplied to a block to be processed 101 to obtain a Gaussian block 102after the first filter. Gaussian filter is applied to the Gaussian block102 after the first filter to obtain a Gaussian block 103 after thesecond filter. Gaussian filter is applied to the Gaussian block 103after the second filter to obtain a Gaussian block 104 after the thirdfilter. Gaussian filter is applied to the Gaussian block 104 after thethird filter to obtain a Gaussian block 105 after the fourth filter.

Then, the Gaussian block 105 after the fourth filter is scaled (a knownmethod may be used for scaling, for example, when a 16-pixel×16-pixelblock is scaled to 8-pixel×8-pixel, a half of pixels can be selected inthe directions of the length and width of the block each), and a fifthfilter is performed to obtain a Gaussian block 106 after the fifthfilter. Gaussian filter is applied to the Gaussian block 106 after thefifth filter to obtain a Gaussian block 107 after the sixth filter.Gaussian filter is applied to the Gaussian block 107 after the sixthfilter to obtain a Gaussian block 108 after the seventh filter. Gaussianfilter is applied to the Gaussian block 108 after the seventh filter toobtain a Gaussian block 109 after the eighth filter. Gaussian filter isapplied to the Gaussian block 109 after the eighth filter to obtain aGaussian block 110 after the ninth filter.

Then, by calculating a difference of various pixel values correspondingto an adjacent-layer block in the multi-layer block, a Gaussiandifference block between adjacent-layer blocks is obtained so as to forma multi-layer Gaussian difference block.

As shown in FIG. 2, a Gaussian difference block between the block to beprocessed 101 and the Gaussian block 102 after the first filter isdenoted with 201. A Gaussian difference block between the Gaussian block102 after the first filter and the Gaussian block 103 after the secondfilter is denoted with 202 . . . and a Gaussian difference block betweenthe Gaussian block 109 after the eighth filter and the Gaussian block110 after the ninth filter is denoted with 208. The Gaussian differenceblocks 201-208 form a multi-layer Gaussian difference block.

Then a key point in the block to be processed 101 is marked. Theparticular method is: with regard to a specific pixel in aspecific-layer Gaussian difference block in the multi-layer Gaussiandifference block, judging whether the value of the specific pixel is amaximum pixel value or a minimum pixel value in a3-pixel×3-pixel×3-pixel cube with the specific pixel as the center ofthe specific-layer Gaussian difference block, an upper-layer Gaussiandifference block and a lower-layer Gaussian difference block, and ifyes, marking the location of the specific pixel as a key point, whereinwith regard to the multi-layer Gaussian difference block, it is assumedthat m key points are marked together.

For example, with regard to a specific pixel in a second-layer Gaussiandifference block 202, if in a 3-pixel×3-pixel×3-pixel cube with thespecific pixel as the center of a first-layer Gaussian difference block201, a second-layer Gaussian difference block 202 and a third-layerGaussian difference block 203, the value of the specific pixel is themaximum or the minimum, then the location of the specific pixel ismarked as a key point. Similar judgment is performed on each pixel inthe second-layer Gaussian difference block 202, and several key pointsare marked in the second-layer Gaussian difference block 202. Similarjudgment is performed on each pixel in the third-layer Gaussiandifference block 203, a sixth-layer Gaussian difference block 206 and aseventh-layer Gaussian difference block 207, and then several key pointsare marked in these blocks. Since the first-layer Gaussian differenceblock 201 does not have a lower-layer Gaussian difference block, aneighth-layer Gaussian difference block does not have an upper-layerGaussian difference block, a pixel of an upper-layer Gaussian differenceblock of a fourth-layer Gaussian difference block 204 does notcorrespond to that of the fourth-layer Gaussian difference block 204 dueto scaling, and, a pixel of a lower-layer Gaussian difference block of afifth-layer Gaussian difference block 205 does not correspond to that ofthe fifth-layer Gaussian difference block 205, there is no key pointmarked for these Gaussian difference blocks. The key points marked inthe second, third, sixth, and seventh-layer Gaussian difference blocksare gathered, and it is assumed that m key points are marked together.

Then, with regard to any key point in the m key points, a gradient valueand a gradient direction of a pixel in a specific adjacent area in aplurality of adjacent areas thereof are calculated, wherein the gradientdirection is approximated to one of a pre-specified plurality ofstandard directions. A total gradient value of a pixel in the specificadjacent area of the key point in a specific standard direction iscalculated to be taken as a component of the SIFT feature vector of thekey point in the adjacent area in the specific standard direction. TheSIFT feature vector is a statistical histogram of the total gradientvalue of the pixel in each standard direction in each adjacent area ofthe key point.

As shown in FIG. 3, in an example, with regard to a key point 301, it istaken as an original point of a coordinate system to make an x axis anda y axis so as to form four quadrants. In each quadrant, a square with a4-pixel side length is made by taking the key point 301 as a vertex.Four squares obtained respectively in four quadrants are taken as fouradjacent areas 302 a-d. With regard to each adjacent area in fouradjacent areas, a gradient value and a gradient direction of each pixeltherein are calculated, and the calculated gradient direction isapproximated to one of eight pre-specified standard directions. It isassumed that eight standard directions are specified: 0 degree, 45degrees, 90 degrees, 135 degrees, 180 degrees, 225 degrees, 270 degreesand 315 degrees. Thus, 22.5 degrees-67.5 degrees can approximate to 45degrees, and 67.5 degrees-112.5 degrees can approximate to 90 degrees,and so on. With regard to a specific adjacent area, for example, 302 c,the gradient values of all the pixels with a pixel gradient direction of0 degree are accumulated, and the gradient values of all the pixels witha pixel gradient direction of 45 degrees are accumulated, and so on, anda statistical histogram of a pixel total gradient value in variousstandard directions of 0 degree, 45 degrees, 90 degrees, 135 degrees,180 degrees, 225 degrees, 270 degrees and 315 degrees in the adjacentarea 302 c is obtained and is denoted with 303. With regard to the keypoint 301, the SIFT feature vector is a statistical histogram of thepixel total gradient value in each standard direction in variousadjacent areas 302 a-d, which is denoted with 304.

With regard to each key point in the m key points, a correspondingstatistical histogram 304 can be obtained. Thus, m SIFT feature vectors41 of a block to be processed are calculated.

Calculation of LBP Feature Vectors:

Firstly, with regard to a pixel in a block to be processed, by comparingan intensity value of the pixel with the intensity values of the otherpixels in a 3-pixel×3-pixel square with the pixel as the center, an8-bit LBP code composed of 0 or 1 is formed, wherein a value range ofthe LBP code is an integer from 0 to 255.

As shown in FIG. 5, it is assumed that an intensity value of a certainpixel 51 in a block to be processed is 140. The remaining eight pixelsin a 3-pixel×3-pixel square with the pixel 51 as the center respectivelyare 95, 200, 199, 178, 18, 10, 128 and 170. 140 is compared with 95,200, 199, 178, 18, 10, 128 and 170 respectively. If the former isgreater than the latter, a value 0 is marked, otherwise, a value 1 ismarked. Lastly, it is obtained that an LBP code (01110001)=113.

Then, the block to be processed is segmented into windows with a fixedsize (such as 64-pixel×64-pixel), wherein the block is segmented into nwindows with a fixed size.

Since the size of the window is fixed and the size of the block to beprocessed is not fixed, the number of n changes with the size of theblock to be processed.

Lastly, the LBP feature vector of each window is calculated, wherein theLBP feature vector is a distribution histogram from 0 to 255 of an LBPcode value of a pixel in the window.

With regard to a specific window, each pixel thereof has an LBP codevalue from 0 to 255. The horizontal axis in FIG. 6 represents the LBPcode value, and the vertical axis represents how many pixels have theLBP code value in the specific window. Therefore, FIG. 6 shows adistribution histogram 305 from 0 to 255 of an LBP code value of a pixelin a specific window. Since the block to be processed has n windows, theblock to be processed has n LBP feature vectors 305, as shown in FIG. 7.All the n LBP feature vectors are denoted with 42.

The Bag-Of-Words Model is Applied to the SIFT Feature Vectors and theLBP Feature Vectors:

The m SIFT feature vectors are classified into k1 clusters and the n LBPfeature vectors are classified into k2 clusters so as to obtain astatistical histogram vector of the k1 clusters of the SIFT featurevectors and a statistical histogram vector of the k2 clusters of the LBPfeature vectors, where k1 and k2 are fixed positive integers. Thesignificance of doing this is that since the number m of the SIFTfeature vectors and the number n of the LBP feature vectors obtainedfrom different blocks to be processed are all different, only if thevectors are classified into a fixed number of clusters so as to obtain astatistical histogram vector of the fixed number of clusters, can thestatistical histogram vector have a comparability between various blocksto be processed.

The prior art has many clustering methods. Taking an SIFT feature vectoras an example, for example, with regard to a sample set containing manySIFT feature vectors, a similarity between various SIFT feature vectorsis calculated and these SIFT feature vectors are clustered according tothe similarity by using a known clustering algorithm in the art toobtain k1 clusters. These SIFT feature vectors are respectivelyclustered into one cluster in the k1 clusters. When a new SIFT featurevector is received, a similarity of the new SIFT feature vector and aSIFT feature vector in a certain cluster in the k1 clusters is voted soas to classify the vector into one cluster. FIG. 8 shows a statisticalhistogram vector of classifying m SIFT feature vectors into k1 clusters,i.e., a statistical histogram vector 801 of a block to be processed withregard to k1 clusters, and it shows with regard to each of the k1clusters, how many SIFT feature vectors belong to the cluster.Similarly, a statistical histogram vector of classifying n LBP featurevectors into k2 clusters can be made, i.e., a statistical histogramvector of a block to be processed with regard to k2 clusters.

The Statistical Histogram Vector after the SIFT Feature Vectors areClustered and the Statistical Histogram Vector after the LBP FeatureVectors are Clustered are Combined:

Firstly, the statistical histogram vector of the k1 clusters of the SIFTfeature vectors and the statistical histogram vector of the k2 clustersof the LBP feature vectors are combined. Then, the combined statisticalhistogram vector is taken as a feature in a feature set of blockprocessing, and the block to be processed is processed.

The statistical histogram vector after the LBP feature vectors areclustered can represent the shape of a foreground portion (such as avisible element) in the block to be processed. The statistical histogramvector after the SIFT feature vectors are clustered aims at localtexture of the foreground portion, etc. The embodiments of the presentinvention comprehensively use the advantages of the statisticalhistogram vector after the LBP feature vectors are clustered and thestatistical histogram vector after the SIFT feature vectors areclustered. The contribution rate of the two portions of features for theentirety can be adjusted by adjusting the ratio of k1 and k2. Forexample, in an application for classifying a cast, an epithelium and ablock of a background that is easy to be confused with a visibleelement, k1>k2 can be set.

Grayscale Conversion:

In order to reduce the influence of color, etc. on the processing resultof a block to be processed, in the embodiments of the present invention,before the SIFT feature vectors and the LBP feature vectors of a blockto be processed are calculated, the block to be processed is firstlyconverted into a grayscale block to be processed and then the SIFTfeature vectors and the LBP feature vectors of the grayscale block to beprocessed are calculated. In this way, the robustness of the processingresult for color, etc. is improved.

The method for processing a block to be processed in the embodiments ofthe present invention is applied to the processing of various blocks,and in particular to the processing of a cast, an epithelium and a blockof a background that is easy to be confused with a visible element. Thisis because maxicells such as a cast and an epithelium have a largeintra-class difference and a high inter-class similarity, and the size,shape, appearance, texture and direction, etc. between casts have agreat difference.

Other Variants

Although in the above-mentioned embodiments, the local feature vectorcomprises two statistical vectors, a SIFT feature vector and an LBPfeature vector, the vector actually can also comprise other localfeature statistical vectors and local feature non-statistical vectors.Only if the local features can be reflected, can the robustness ofprocessing one or more of the size, shape, appearance, texture anddirection and illumination and color of a visible element be improved.Experiments show that the SIFT feature vector and the LBP feature vectorare two relatively good local feature vectors.

Although the statistical histogram vector after the SIFT feature vectorsare clustered and the statistical histogram vector after the LBP featurevectors are clustered are combined in the above-mentioned embodiments,those skilled in the art should understand that, for example, theabove-mentioned combination process does not exist under the conditionwhere there is only one local feature vector (such as a SIFT featurevector).

Although in the above-mentioned embodiments, in order to reduce theinfluence of color, etc. on the processing result, before the SIFTfeature vectors and the LBP feature vectors of a block to be processedare calculated, the block to be processed is firstly converted into agrayscale block to be processed, those skilled in the art shouldunderstand that this grayscale conversion may not be performed.

Although in the above-mentioned embodiments, m SIFT feature vectors andn LBP feature vectors are calculated by means of a specific process;however, the SIFT feature vectors and the LBP feature vectors are knownconcepts, the vectors are not merely used for processing a block to beprocessed all the time. It is known to those skilled in the related artthat a calculation process of the vectors may have various variants. Forexample, a number of scalings can be performed in the process ofapplying a number of Gaussian filters, the number of Gaussian filterscan be adjusted, the size of a window can be taken as 32-pixel×32-pixelor 16-pixel×16-pixel when calculating the LBP feature vector, etc.

As shown in FIG. 9, an apparatus 10 for processing a block to beprocessed of a urine sediment image according to one embodiment of thepresent invention comprises a calculation unit 1001, a classificationunit 1002 and a processing unit 1003. The calculation unit 1001 isconfigured to calculate a variable number of local feature vectors of ablock to be processed, wherein the local feature vector is a vectorrepresenting a feature of a local location of the block to be processed.The classification unit 1002 is configured to classify the variablenumber of local feature vectors into a fixed number of clusters so as toobtain a statistical histogram vector of the fixed number of clusters ofthe block to be processed, wherein the statistical histogram vectorreflects the statistical distribution of the variable number of localfeature vectors in the fixed number of clusters. The processing unit1003 is configured to take the statistical histogram vector as a featurein a feature set of block processing and process the block to beprocessed. The apparatus 10 can be realized using software, hardware(e.g., an integrated circuit, a FPGA, etc.) or a combination of softwareand hardware.

In addition, the variable number of local feature vectors may comprise mSIFT feature vectors and n LBP feature vectors, where m and n arevariable positive integers. The classification unit 1002 may be furtherconfigured to: classify the m SIFT feature vectors into k1 clusters andclassify the n LBP feature vectors into k2 clusters so as to obtain astatistical histogram vector of the k1 clusters of the SIFT featurevectors and a statistical histogram vector of the k2 clusters of the LBPfeature vectors, where k1 and k2 are fixed positive integers.

In addition, the processing unit 1003 may be further configured to:combine the statistical histogram vector of the k1 clusters of the SIFTfeature vectors and the statistical histogram vector of the k2 clustersof the LBP feature vectors; and take the combined statistical histogramvector as a feature in a feature set of block processing and process theblock to be processed.

In addition, the calculation unit 1001 may be further configured to:convert the block to be processed into a grayscale block to beprocessed, and calculate a variable number of local feature vectors ofthe grayscale block to be processed.

In addition, the block to be processed may comprise a cast, anepithelium and a block of a background that is easy to be confused witha visible element.

In addition, the calculation unit 1001 may be further configured tocalculate the m SIFT feature vectors of the block to be processed asfollows: applying a successive Gaussian filter to the block to beprocessed a plurality of times and/or a scaled block to be processed soas to obtain a multi-layer block; by calculating a difference of variouspixel values corresponding to an adjacent-layer block in the multi-layerblock, obtaining a Gaussian difference block between adjacent-layerblocks so as to form a multi-layer Gaussian difference block; withregard to a specific pixel in a specific-layer Gaussian difference blockin the multi-layer Gaussian difference block, judging whether the valueof the specific pixel is a maximum pixel value or a minimum pixel valuein a 3-pixel×3-pixel×3-pixel cube with the specific pixel as the centerof the specific-layer Gaussian difference block, an upper-layer Gaussiandifference block and a lower-layer Gaussian difference block, and ifyes, marking the location of the specific pixel as a key point, whereinwith regard to the multi-layer Gaussian difference block, m key pointsare marked together; calculating a gradient value and a gradientdirection of a pixel in a specific adjacent area in a plurality ofadjacent areas of the key point, wherein the gradient direction isapproximated to one of a pre-specified plurality of standard directions;and calculating a total gradient value of a pixel in the specificadjacent area of the key point in a specific standard direction to betaken as a component of the SIFT feature vector of the key point in thespecific adjacent area in the specific standard direction.

In addition, the calculation unit 1001 may be further configured tocalculate the n LBP feature vectors of the block to be processed asfollows: with regard to a pixel in the block to be processed, bycomparing an intensity value of the pixel with the intensity values ofthe other pixels in a 3-pixel×3-pixel square with the pixel as thecenter, forming an 8-bit LBP code composed of 0 or 1, wherein a valuerange of the LBP code is an integer from 0 to 255; segmenting the blockto be processed into windows with a fixed size, wherein the block issegmented into n windows with a fixed size; and calculating an LBPfeature vector of each window, wherein the LBP feature vector is adistribution histogram from 0 to 255 of an LBP code value of a pixel inthe window.

FIG. 10 shows a device 11 for processing a block to be processed of aurine sediment image according to one embodiment of the presentinvention. The device 11 may comprise a memory 1101 and a processor1102. The memory 1101 is used for storing an executable instruction. Theprocessor 1102 is used for performing an operation performed by eachunit in the apparatus 10 according to the executable instruction storedin the memory.

In addition, one embodiment of the present invention further provides amachine-readable medium on which an executable instruction is stored,when the executable instruction is executed, a machine is caused toperform an operation performed by the processor 1102.

Those skilled in the art should understand that various variations andmodifications can be made to the above various embodiments withoutdeparting from the spirit of the present invention. Therefore, the scopeof protection of the present invention should be defined by the appendedclaims.

1. A method for processing a block to be processed of a urine sedimentimage, comprising: calculating a variable number of local featurevectors of a block to be processed, wherein the local feature vector isa vector representing a feature of a local location of the block to beprocessed; classifying the variable number of local feature vectors intoa fixed number of clusters so as to obtain a statistical histogramvector of the fixed number of clusters of the block to be processed,wherein the statistical histogram vector reflects the statisticaldistribution of the variable number of local feature statistical vectorsin the fixed number of clusters; and taking the statistical histogramvector as a feature in a feature set of block processing and processingthe block to be processed.
 2. The method as claimed in claim 1,characterized in that the variable number of local feature vectorscomprise m scale invariant feature transform (SIFT) feature vectors andn local binary pattern (LBP) feature vectors, where m and n are variablepositive integers, and the step of classifying the variable number oflocal feature statistical vectors into a fixed number of clusterscomprises: classifying the m SIFT feature vectors into k1 clusters andclassifying the n LBP feature vectors into k2 clusters so as to obtain astatistical histogram vector of the k1 clusters of the SIFT featurevectors and a statistical histogram vector of the k2 clusters of the LBPfeature vectors, where k1 and k2 are fixed positive integers.
 3. Themethod as claimed in claim 2, characterized in that the step of takingthe statistical histogram vector as a feature in a feature set of blockprocessing and processing the block to be processed comprises: combiningthe statistical histogram vector of the k1 clusters of the SIFT featurevectors and the statistical histogram vector of the k2 clusters of theLBP feature vectors; and taking the combined statistical histogramvector as a feature in a feature set of block processing and processingthe block to be processed.
 4. The method as claimed in claim 1,characterized in that the step of calculating a variable number of localfeature vectors of a block to be processed comprises: converting theblock to be processed into a grayscale block to be processed, andcalculating a variable number of local feature vectors of the grayscaleblock to be processed.
 5. The method as claimed in claim 1,characterized in that the block to be processed comprises a cast, anepithelium and a block of a background that is easy to be confused witha visible element.
 6. The method as claimed in claim 2, characterized inthat in the step of calculating a variable number of local featurevectors of a block to be processed, the m SIFT feature vectors of theblock to be processed are calculated as follows: applying a successiveGaussian filter to the block to be processed a plurality of times and/ora scaled block to be processed so as to obtain a multi-layer block; bycalculating a difference of various pixel values corresponding to anadjacent-layer block in the multi-layer block, obtaining a Gaussiandifference block between adjacent-layer blocks so as to form amulti-layer Gaussian difference block; with regard to a specific pixelin a specific-layer Gaussian difference block in the multi-layerGaussian difference block, judging whether the value of the specificpixel is a maximum pixel value or a minimum pixel value in a3-pixel×3-pixel×3-pixel cube with the specific pixel as the center ofthe specific-layer Gaussian difference block, an upper-layer Gaussiandifference block and a lower-layer Gaussian difference block, and ifyes, marking the location of the specific pixel as a key point, whereinwith regard to the multi-layer Gaussian difference block, m key pointsare marked altogether; calculating a gradient value and a gradientdirection of a pixel in a specific adjacent area in a plurality ofadjacent areas of the key point, wherein the gradient direction isapproximated to one of a pre-specified plurality of standard directions;and calculating a total gradient value of a pixel in the specificadjacent area of the key point in a specific standard direction to betaken as a component of the SIFT feature vector of the key point in thespecific adjacent area in the specific standard direction.
 7. The methodas claimed in claim 2, characterized in that in the step of calculatinga variable number of local feature vectors of a block to be processed,the n LBP feature vectors of the block to be processed are calculated asfollows: with regard to a pixel in the block to be processed, bycomparing an intensity value of the pixel with the intensity values ofthe other pixels in a 3-pixel×3-pixel square with the pixel as thecenter, forming an 8-bit LBP code composed of 0 or 1, wherein a valuerange of the LBP code is an integer from 0 to 255; segmenting the blockto be processed into windows with a fixed size, wherein the block issegmented into n windows with a fixed size; and calculating an LBPfeature vector of each window, wherein the LBP feature vector is adistribution histogram from 0 to 255 of an LBP code value of a pixel inthe window.
 8. An apparatus for processing a block to be processed of aurine sediment image, comprising: a calculation unit configured tocalculate a variable number of local feature vectors of a block to beprocessed, wherein the local feature vector is a vector representing afeature of a local location of the block to be processed; aclassification unit configured to classify the variable number of localfeature statistical vectors into a fixed number of clusters so as toobtain a statistical histogram vector of the fixed number of clusters ofthe block to be processed, wherein the statistical histogram vectorreflects the statistical distribution of the variable number of localfeature statistical vectors in the fixed number of clusters; and aprocessing unit configured to take the statistical histogram vector as afeature in a feature set of block processing and process the block to beprocessed.
 9. The apparatus as claimed in claim 8, characterized in thatthe variable number of local feature vectors comprise m scale invariantfeature transform (SIFT) feature vectors and n local binary pattern(LBP) feature vectors, where m and n are variable positive integers, andthe classification unit is further configured to: classify the m SIFTfeature vectors into k1 clusters and classify the n LBP feature vectorsinto k2 clusters so as to obtain a statistical histogram vector of thek1 clusters of the SIFT feature vectors and a statistical histogramvector of the k2 clusters of the LBP feature vectors, where k1 and k2are fixed positive integers.
 10. The apparatus as claimed in claim 9,characterized in that the processing unit is further configured to:combine the statistical histogram vector of the k1 clusters of the SIFTfeature vectors and the statistical histogram vector of the k2 clustersof the LBP feature vectors; and take the combined statistical histogramvector as a feature in a feature set of block processing and process theblock to be processed.
 11. The apparatus as claimed in claim 8,characterized in that the calculation unit is further configured to:convert the block to be processed into a grayscale block to beprocessed, and calculate a variable number of local feature vectors ofthe grayscale block to be processed.
 12. The apparatus as claimed inclaim 8, characterized in that the block to be processed comprises acast, an epithelium and a block of a background that is easy to beconfused with a visible element.
 13. The apparatus as claimed in claim9, characterized in that the calculation unit is further configured tocalculate the m SIFT feature vectors of the block to be processed asfollows: applying a successive Gaussian filter to the block to beprocessed a plurality of times and/or a scaled block to be processed soas to obtain a multi-layer block; by calculating a difference of variouspixel values of an adjacent-layer block in the multi-layer block,obtaining a Gaussian difference block between adjacent-layer blocks soas to form a multi-layer Gaussian difference block; with regard to aspecific pixel in a specific-layer Gaussian difference block in themulti-layer Gaussian difference block, judging whether the value of thespecific pixel is a maximum pixel value or a minimum pixel value in a3-pixel×3-pixel×3-pixel cube with the specific pixel as the center ofthe specific-layer Gaussian difference block, an upper-layer Gaussiandifference block and a lower-layer Gaussian difference block, and ifyes, marking the location of the specific pixel as a key point, whereinwith regard to the multi-layer Gaussian difference block, m key pointsare marked together; calculating a gradient value and a gradientdirection of a pixel in a specific adjacent area in a plurality ofadjacent areas of the key point, wherein the gradient direction isapproximated to one of a pre-specified plurality of standard directions;and calculating a total gradient value of a pixel in the specificadjacent area of the key point in a specific standard direction to betaken as a component of the SIFT feature vector of the key point in thespecific adjacent area in the specific standard direction.
 14. Theapparatus as claimed in claim 9, characterized in that the calculationunit is further configured to calculate the n LBP feature vectors of theblock to be processed as follows: with regard to a pixel in the block tobe processed, by comparing an intensity value of the pixel with theintensity values of the other pixels in a 3-pixel×3-pixel square withthe pixel as the center, forming an 8-bit LBP code composed of 0 or 1,wherein a value range of the LBP code is an integer from 0 to 255;segmenting the block to be processed into windows with a fixed size,wherein the block is segmented into n windows with a fixed size; andcalculating an LBP feature vector of each window, wherein the LBPfeature vector is a distribution histogram from 0 to 255 of an LBP codevalue of a pixel in the window.
 15. A device for processing a block tobe processed of a urine sediment image, comprising: a memory for storingexecutable instructions, the executable instructions, when executed,implementing the method of claim 1; and a processor for executing theexecutable instructions.
 16. A machine-readable medium on which anexecutable instruction is stored, wherein when the executableinstruction is executed, a machine is caused to perform the method ofclaim 1.